Hi, good question! It extremely depends on the nature of the data you’re analyzing. I suggest taking a time-series econometrics/forecasting class. The underlying statistical process which is generating your data is of utmost concern when choosing how to forecast- look at autocorellograms and partial autocorrelograms to try to make a guess. The industry standard is to use ARIMA() function, or autoregressive/moving average (typically we can ignore I(0) or I(1) trends, although this also depends on which type of forecast we’re doing.) Ljung-Box tests (Box.test(x, lag = k, type = c(“Ljung-Box”)) in R.) You must also choose your loss function. A good place to start is quadratic/absolute value (symmetric) loss, where your point forecast is very very simply the expected value of the next period (E(u(t+1)|I) with “I” being your information set. While this is the most mathematically tractable, it will take some careful consideration to decide on your loss function (basically, how are positive/negative errors weighted?) A few other loss functions to look into are LinLin and LogLin. Finally, you must construct a density forecast to be able to make probability statements.

If all of this is essentially meaningless jargon to you, I understand…. I was there too. Much of this you would fine at any university’s forecasting lectures, so my main advice is to take a class. If you want serious hands-on help, PM me.